Assessing Air Quality and Health Benefits of Enhanced Management of Forests, Shrublands, and Grasslands Against Wildfires in California
Bibliographic record
Abstract
Abstract California wildfires have grown increasingly frequent and intense over recent decades, raising serious public health concerns. In response, the California Air Resources Board (CARB) 2022 Scoping Plan outlines land management strategies to reduce wildfire risk and associated emissions under various climate change scenarios. This study evaluates the health benefits of CARB's official mitigation pathway, the S3 scenario, compared to a business‐as‐usual approach, using three global climate models (GCMs) and three future time slices. We apply the GEOS‐Chem model to estimate fire‐induced PM 2.5 concentrations and use the U.S. EPA's BenMAP‐CE tool, along with a wildfire‐specific chronic mortality dose‐response function, to assess associated morbidity and mortality. Results suggest that S3 can significantly reduce fire‐related PM 2.5 exposure, particularly in northern and central California where concentrations are typically highest—and where S3 treatments are most effective. In 2035 under the second generation Canadian Earth System Model GCM, for instance, S3 is associated with 1,927 fewer premature deaths and substantial reductions in asthma‐ and respiratory‐related emergency room visits. However, health benefits vary by GCM and year, underscoring the influence of meteorological conditions on fire activity and health outcomes. These findings point to the importance of strategically timed and located land management actions and integrating climate variability into future mitigation planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".